Gentleman-Programming/engram

▲ 199 stars today★ 6,633⑂ 682

Persistent memory system for AI coding agents. Agent-agnostic Go binary with SQLite + FTS5, MCP server, HTTP API, CLI, and TUI.

About Gentleman-Programming/engram

Gentleman-Programming/engram is an open-source project on GitHub, mainly written in Go. Persistent memory system for AI coding agents. Agent-agnostic Go binary with SQLite + FTS5, MCP server, HTTP API, CLI, and TUI. It currently holds 6,633 stars and 682 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).

Project Overview

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GitHub Repository Details

Repository Gentleman-Programming/engram · default branch - · size 0 KB · watchers 0 · source: GitHub REST API and repository README

README

https://github.com/Gentleman-Programming/engram/blob/HEAD/Engram — One Brain. Local or Cloud.

Persistent memory for AI coding agents
One brain. Local or cloud. Agent-agnostic, single binary, zero dependencies.

WebsiteGentle-AIGentle-AI Wiki

InstallationRelease PolicyEngram CloudAgent SetupCodebase GuideArchitecturePluginsTeam UsageContributingFull Docs

https://github.com/Gentleman-Programming/engram/blob/HEAD/Star History Chart

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engram /ˈen.ɡræm/ — _neuroscience_: the physical trace of a memory in the brain.

Your AI coding agent forgets everything when the session ends. Engram gives it a brain.

A Go binary with SQLite + FTS5 full-text search, exposed through CLI, HTTP API, MCP, and an interactive TUI. It works with any MCP-compatible agent, including Claude Code, OpenCode, Gemini CLI, Codex, VS Code (Copilot), Antigravity, Cursor, and Windsurf.

No Node.js, Python, or Docker is required: one binary, one SQLite file.

Agent (Claude Code / OpenCode / Gemini CLI / Codex / VS Code / Antigravity / ...)
    ↓ MCP stdio
Engram (single Go binary)
    ↓
SQLite + FTS5 (~/.engram/engram.db)

For agents

Treat Engram as a curated project memory, not a transcript sink. Use this operating contract throughout the session.

1. Orient before writing. Start with mem_current_project to confirm the resolved project and its source. At the start of related work, use mem_context and mem_search to recover the relevant history. 2. Search before repeating. Before revisiting a decision, bug, convention, or request that may already be known, search with focused terms. Search results are previews, not the complete record. 3. Retrieve progressively. Use mem_search for candidates, mem_timeline when surrounding session context matters, and mem_get_observation before relying on a full observation. 4. Save significant knowledge deliberately. Save completed bug fixes, decisions, discoveries, configuration changes, patterns, and durable user constraints with mem_save. Do not capture raw tool output or every conversational turn. 5. Keep evolving knowledge stable. Give an evolving topic a stable topic_key such as architecture/auth-model; reuse it to update that topic rather than creating competing memories. Use mem_suggest_topic_key when the key is unclear. 6. Leave a handoff. Before ending a session, save a mem_session_summary with the goal, instructions, discoveries, accomplished work, next steps, and relevant files. 7. Recover after compaction. Persist the compacted handoff with mem_session_summary first. Then call mem_context to recover recent session history before continuing.

A useful memory is structured

What: Added retry-safe upload handling.
Why: Retries could create duplicate records.
Where: internal/upload/handler.go
Learned: Reuse the request id as the idempotency key.

Use a short, searchable title and a fitting type with that content. The full Memory Protocol defines the durable-save rules and session-summary shape.

Choose MCP tools by intent

Tool availability can vary by MCP profile. Start with the intent, then use your client's tool discovery mechanism (such as ToolSearch) only when a deferred tool is needed.

| Intent | Start with | | --- | --- | | Confirm the project and recover recent work | mem_current_project, mem_context | | Find prior knowledge without repeating work | mem_search | | Inspect a result in enough detail | mem_timeline, mem_get_observation | | Save or refine durable knowledge | mem_save, mem_update, mem_suggest_topic_key | | Preserve the user's request | mem_save_prompt | | Hand off or close a session | mem_session_summary, mem_session_start, mem_session_end | | Review stale knowledge or memory relationships | mem_review, mem_judge, mem_compare | | Diagnose project or store state | mem_doctor |

For parameters and the complete, current tool reference, see the full documentation.

Quick start

Install

For production use and security support, install the latest stable release from GitHub Releases. Release candidates are prerelease validation and feedback builds; choose one only when you accept prerelease risk. See the Release Policy before upgrading.

Homebrew remains on the stable v1.20.0 line:

brew install gentleman-programming/tap/engram

For Windows, Linux, source builds, and all installation methods, see Installation.

Set up your agent

Run the setup command for the agent you use, then restart that agent. engram setup writes the applicable MCP and integration configuration; it does not require you to start a server for the usual stdio-only setup.

| Agent | Setup | | --- | --- | | Claude Code | claude plugin marketplace add Gentleman-Programming/engram && claude plugin install engram | | Pi | engram setup pi | | OpenCode | engram setup opencode | | Gemini CLI | engram setup gemini-cli | | Codex | engram setup codex | | Antigravity CLI | engram setup antigravity-cli | | Windsurf | engram setup windsurf | | Qwen Code | engram setup qwen | | Kiro | engram setup kiro | | Cursor | engram setup cursor | | VS Code (Copilot) | engram setup vscode-copilot | | Kilo Code | engram setup kilocode | | Another MCP-compatible agent | Manual MCP setup |

See Agent Setup for per-agent configuration, plugin behavior, manual MCP setup, compaction resilience, and troubleshooting. Pi users can also find the package at gentle-engram.

Local first, portable when needed

Engram keeps memory local by default. The local SQLite database is authoritative; Git Sync exports portable compressed chunks for sharing across machines, and Engram Cloud is optional, project-scoped replication/shared access with browser visibility.

| Need | Start here | | --- | --- | | Local memory and the runtime model | Architecture | | Share memory with Git | Git Sync reference | | Use optional Cloud replication | Engram Cloud | | Diagnose or recover Cloud operations | Cloud troubleshooting |

For an existing local database, use the guided upgrade sequence. If the dry run reports changes, apply them before bootstrap; otherwise continue directly to bootstrap.

engram cloud upgrade doctor --project 
engram cloud upgrade repair --project  --dry-run
engram cloud upgrade repair --project  --apply # only when the dry run reports changes
engram cloud upgrade bootstrap --project 
engram cloud upgrade status --project 

See the Cloud upgrade reference for apply, rollback, and recovery details.

Project-aware reads

Project-aware reads use the canonical current project when no selector is supplied: an explicit project, then ENGRAM_PROJECT, then cwd detection. Use --all in the CLI or all_projects=true in HTTP for an intentional global read; do not combine either with an explicit project. engram context retains its positional project as an alias for --project. GET /sync/status supports one resolved project and rejects all_projects=true because its provider cannot aggregate status.

Terminal UI

engram tui

https://github.com/Gentleman-Programming/engram/blob/HEAD/TUI Dashboard https://github.com/Gentleman-Programming/engram/blob/HEAD/TUI recent observations https://github.com/Gentleman-Programming/engram/blob/HEAD/TUI Observation Detail https://github.com/Gentleman-Programming/engram/blob/HEAD/TUI Search Results

Navigate with j/k, use Enter to drill in, c to copy content to the clipboard, / to search, and Esc to go back. The TUI uses the Catppuccin Mocha theme.

Documentation

| Doc | Description | | --- | --- | | Installation | Platform support and all installation methods | | Release Policy | Stable, RC, security-support, upgrade, and rollback guidance | | Agent Setup | Per-agent configuration and compaction resilience | | Intended Usage | The human mental model for using Engram | | Architecture | Memory model, tool behavior, and project structure | | Codebase Guide | Repository structure, flows, and implementation landmarks | | Plugins | OpenCode and Claude Code plugin details | | Team Usage | Shared-memory conventions | | Engram Cloud | Cloud quickstart, deployment, and technical links | | Doctor | Operational diagnosis and repair workflows | | Binary self-testing | Isolated reliability and performance checks for released binaries | | Beta Testing | Isolated beta testing flows and cleanup guidance | | Comparison | Engram compared with claude-mem | | Obsidian Brain | Export memories as an Obsidian knowledge graph (beta) | | Full Docs | Complete CLI, environment, API, and operational reference |

Dashboard contributors: if you modify .templ files in internal/cloud/dashboard/, run make templ to regenerate before committing. See Dashboard templ regeneration.

Contributing

Every change starts with an approved issue. See Contributing for the issue-first workflow, labels, review requirements, and contributor standards.

Trademark notice: The Engram names and logos are trademarks of Alan Buscaglia. The MIT License applies to the code; it does not permit implying endorsement or official affiliation. See TRADEMARKS.md.

License

MIT

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Inspired by claude-mem — but agent-agnostic, simpler, and built different.

Contributors

GitHub Stars & Activity

6,633Stars
682Forks
0Open issues
GoLanguage

GitHub Popularity

GitHub stars6,633
Forks682
Open issues0
Primary languageGo
License-
Stars gained today199
Created-
Last pushed-

Trending History

Weekly boardrank #68 · ▲ 199 stars

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